A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Multiresolution Minimization of Renyi's Mutual Information for fetal-ECG Extraction
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Multiresolution ICA for artifact identification from electroencephalographic recordings
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Artifact cancellation from electrocardiogram by mixed Wavelet-ICA filter
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Segmentation and classification of vowel phonemes of assamese speech using a hybrid neural framework
Applied Computational Intelligence and Soft Computing
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Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.